Vishwakarma Institute of Technology Master of Computer ...
Vishwakarma Institute of Technology Master of Computer ... Vishwakarma Institute of Technology Master of Computer ...
BRACT’S Vishwakarma Institute of Technology, Pune – 411 037 Department of Computer Engineering Structure & Syllabus of MCA Pattern C11, issue 03, Rev 01 Dt 2/4/2011 22 FF No. : 654 CS72110::DATA WAREHOUSING AND DATA MINING Credits: 03 Teaching Scheme: - Theory 3 Hrs/Week Prerequisites: DBMS Objectives: - • To understand the process of data mining and the key steps involved well enough to lead/manage a real-life data mining project • Know the basics of data warehousing and how it facilitates data mining • To understand fundamental issues in statistical data analysis that cut across all procedures, such as generalization to other data, basic tradeoffs, and validity of models. • To deliver an overview of web data mining and other significant mining techniques Unit I (9+2 Hrs) Introduction A. Difference between operational database systems and data warehouses, Use of Data warehouse. A multidimentional data model, schema for multidimentional database: star, snowflake ,fact constellation. Data ware house architecture, types of OLAP server, data warehouse implementation. Diffrence between OLTP and Data Warehouse, Data cube and OLAP, Concept hierarchies: total and partial, Set-grouping hierarchies, OLAP operations: drill-down, Roll-up and extreme Roll-up, slice-dice and pivotmodels of Data warehouse: Enterprise Warehouse, Data Mart, Virtual Warehouse . B. Difference between OLAP and OLTP operations, star-net model, Unit II (9+2 Hrs) Introduction to Data mining A. Data mining primitives, Techniques:- Clustering, classification, association rules, linear and multiple regression, Feature selection, Mining text databases, multimedia databases, data pre processing: data summarization, data cleaning ,data reduction,. B. Text Mining, Mining Spatial ,Data Mining Application
BRACT’S Vishwakarma Institute of Technology, Pune – 411 037 Department of Computer Engineering Unit III (9+2 Hrs) mining frequent pattern A. Basic concept, market basket analysis ,frequent pattern mining, frequent itemset mining methods , mining frequent itemset using candidate generation, mining frequent itemset without candidate generation methods, mining various kind of association rules. B. Mining Frequent Itemset Using Vertical Data Format,Mining Closed Frequent Itemset Unit IV (9+2 Hrs) Classification and Prediction A. Issues regarding classification and prediction ,Decision tree classifier:, baysian classification, rule based classification, neural network classification, back propagation, KNN classifier, classifier accuracy, prediction: linear and non linear regression. B. Support Vector Machines, other classification methods like genetic algorithm, rough set approach, fuzzy set approach. Unit V (9+2 Hrs) Clustering A. What is cluster analysis, types of cluster analysis ,a categorization of major clustering method ,partition ,hierarchical ,density based, grid based method, outlier analysis. B. Constraints based cluster analysis, clustering high dimensional data. Text Books 1. Jiawei Han and Micheline Kamber “Data mining: concepts and techniques”, the Morghan Kaufman, 2001. 2. Abraham Silberschatz, Henry F. Korth, S. Sudarshan, “Database Systems Concepts”, 5 th edition, 2005. Reference Books 1. T. Mitchell. “Machine Learning”, McGraw-Hill, 1997. 2. Hand, Smyth, Mannila “Principles of Data mining”, MIT press,2001. Additional reading 1. Ralf Kimball, “Data warehouse life cycle toolkit”-John Wiley,1998. 2. Gagendra Sharma, “Data mining, Data warehousing and OLAP”, S.K. Kataria and sons, First edition,2007-08. Structure & Syllabus of MCA Pattern C11, issue 03, Rev 01 Dt 2/4/2011 23
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BRACT’S<br />
<strong>Vishwakarma</strong> <strong>Institute</strong> <strong>of</strong> <strong>Technology</strong>, Pune – 411 037<br />
Department <strong>of</strong> <strong>Computer</strong> Engineering<br />
Unit III (9+2 Hrs)<br />
mining frequent pattern<br />
A. Basic concept, market basket analysis ,frequent pattern mining, frequent itemset<br />
mining methods , mining frequent itemset using candidate generation, mining frequent<br />
itemset without candidate generation methods, mining various kind <strong>of</strong> association rules.<br />
B. Mining Frequent Itemset Using Vertical Data Format,Mining Closed Frequent Itemset<br />
Unit IV (9+2 Hrs)<br />
Classification and Prediction<br />
A. Issues regarding classification and prediction ,Decision tree classifier:, baysian<br />
classification, rule based classification, neural network classification, back propagation,<br />
KNN classifier, classifier accuracy, prediction: linear and non linear regression.<br />
B. Support Vector Machines, other classification methods like genetic algorithm, rough<br />
set approach, fuzzy set approach.<br />
Unit V (9+2 Hrs)<br />
Clustering<br />
A. What is cluster analysis, types <strong>of</strong> cluster analysis ,a categorization <strong>of</strong> major clustering<br />
method ,partition ,hierarchical ,density based, grid based method, outlier analysis.<br />
B. Constraints based cluster analysis, clustering high dimensional data.<br />
Text Books<br />
1. Jiawei Han and Micheline Kamber “Data mining: concepts and techniques”, the<br />
Morghan Kaufman, 2001.<br />
2. Abraham Silberschatz, Henry F. Korth, S. Sudarshan, “Database Systems<br />
Concepts”,<br />
5 th edition, 2005.<br />
Reference Books<br />
1. T. Mitchell. “Machine Learning”, McGraw-Hill, 1997.<br />
2. Hand, Smyth, Mannila “Principles <strong>of</strong> Data mining”, MIT press,2001.<br />
Additional reading<br />
1. Ralf Kimball, “Data warehouse life cycle toolkit”-John Wiley,1998.<br />
2. Gagendra Sharma, “Data mining, Data warehousing and OLAP”, S.K. Kataria<br />
and sons, First edition,2007-08.<br />
Structure & Syllabus <strong>of</strong> MCA Pattern C11, issue 03, Rev 01 Dt 2/4/2011<br />
23
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